The goal of multi-source heterogeneous data embedding is to map multiple source information into a continuous low-dimensional latent feature space.
Complementary information from multiple sources will generate features that cannot be captured by the individual source [1]}. Many studies demonstrate that multi-source heterogeneous data embedding deepens the fusion of information from different sources and achieves better performance than the state-of-art uni-source methods [2]}, [3]}, [4]}. For our problem setting, we focus on multi-source data fusion based on multi-graphs, which has been mainly based on cross-source regularization [5]}, [6]}. However, these studies were not tailored for urban scenarios. Before introducing our method for urban regional profiling, let us give some definitions and present the problem statement.
